Recently, few works have been proposed for video-based UDA.
The key idea of these methods is to achieve temporal alignment by aligning both frame- and video-level features through adversarial learning [1]}, [2]}, contrastive learning [3]}, [4]}, attention [5]}, or combining some of these mechanisms, e.g., adversarial learning with attention [6]}, [7]}.
Though they have advanced video-based UDA, there is still room for improvement. Cross-domain videos are highly complex data, containing diverse information like domain-related information, semantic information, temporal information, etc.
Existing works usually conduct feature alignments with diverse information mixed up, and thus may not guarantee that the source and target domains are sufficiently aligned.
